Assessment of Drought risk using multi-sensor drought indices and vulnerability factors: A case study of Illam of Province

Drought is an unpleasant climatic phenomenon that directly affects different dimensions of human societies. In order to know and choose the right management decision, it is necessary to design and develop an integrated approach to more effectively control this phenomenon and provide early warnings.In this study, twelve various remotely sensed indices of the Moderate Resolution Imaging Spectroradiometer (MODIS) and digital elevation model (DEM) were used to monitor drought during 2000–2018 growing season. Standardized Precipitation Index (SPI) with time scales of 1 to 12 months was used as reference data. The relations between thirteen indices and SPI with different time scales were modulated using machine learning approach. The random forest technique was used to construct a comprehensive drought monitoring model in Ilam Province. Validation data were provided based on relative soil moisture, Standardized Precipitation Evapotranspiration Index (SPEI), and crop yield data. It was observed that random forest produced good applicability (R2 = 0.88) for SPI prediction. In the next step, the Drought Hazard Index (DHI) was generated based on the probability occurrences of drought using the comprehensive drought model which was made in the previous step. The Drought Vulnerability Index (DVI) was calculated by using 7 socioeconomic indices. Finally, the Drought Risk Index (DRI) was obtained by multiplying DHI and DVI for Ilam province. The result of the DRI map showed that 2 Counties are at very high risk of drought, 4 Counties are at high risk and 4 Counties are at moderate and low risk of drought. Overall, the result of our study provides a comprehensive method for assessment of regional drought. Also based on this model, Counties with high vulnerability can be identified to provide timely management programs to help improve the situation.